{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data (pre) processing using `DataTransformer` and `Pipeline`\n",
    "\n",
    "In this notebook, we will demonstrate how to perform some common preprocessing tasks using `darts`\n",
    "\n",
    "As a toy example, we will use the [Monthly Milk Production dataset](https://www.kaggle.com/tejasgosavi/monthlymilkproductionpounds). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `DataTransformer` abstraction\n",
    "\n",
    "`DataTransformer` aims to provide a unified way of dealing with transformations of `TimeSeries`:\n",
    "\n",
    "- `transform()` is implemented by all transformers. This method takes in either a `TimeSeries` of a sequence of `TimeSeries`, applies the transformation and returns it as a new `TimeSeries`/sequence of `TimeSeries.\n",
    "- `inverse_transform()` is implemented by transformers for which an inverse transformation function exists. It works in a similar way as `transform()`\n",
    "- `fit()` allows transformers to extract some information from the time series first before calling `transform()` or `inverse_transform()`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up the example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix python path if working locally\n",
    "from utils import fix_pythonpath_if_working_locally\n",
    "\n",
    "fix_pythonpath_if_working_locally()\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from darts import TimeSeries\n",
    "from darts.models import ExponentialSmoothing\n",
    "from darts.dataprocessing.transformers import (\n",
    "    Scaler,\n",
    "    MissingValuesFiller,\n",
    "    Mapper,\n",
    "    InvertibleMapper,\n",
    ")\n",
    "from darts.dataprocessing import Pipeline\n",
    "from darts.metrics import mape\n",
    "from darts.utils.statistics import check_seasonality, plot_acf, plot_residuals_analysis\n",
    "from darts.utils.timeseries_generation import linear_timeseries\n",
    "from darts.datasets import MonthlyMilkDataset, MonthlyMilkIncompleteDataset\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import logging\n",
    "\n",
    "logging.disable(logging.CRITICAL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the data and creating a time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<TimeSeries (DataArray) (Month: 168, component: 1, sample: 1)>\n",
      "array([[[589.]],\n",
      "\n",
      "       [[561.]],\n",
      "\n",
      "       [[640.]],\n",
      "\n",
      "       [[656.]],\n",
      "\n",
      "       [[727.]],\n",
      "\n",
      "       [[697.]],\n",
      "\n",
      "       [[640.]],\n",
      "\n",
      "       [[599.]],\n",
      "\n",
      "       [[568.]],\n",
      "\n",
      "       [[577.]],\n",
      "\n",
      "...\n",
      "\n",
      "       [[892.]],\n",
      "\n",
      "       [[903.]],\n",
      "\n",
      "       [[966.]],\n",
      "\n",
      "       [[937.]],\n",
      "\n",
      "       [[896.]],\n",
      "\n",
      "       [[858.]],\n",
      "\n",
      "       [[817.]],\n",
      "\n",
      "       [[827.]],\n",
      "\n",
      "       [[797.]],\n",
      "\n",
      "       [[843.]]])\n",
      "Coordinates:\n",
      "  * Month      (Month) datetime64[ns] 1962-01-01 1962-02-01 ... 1975-12-01\n",
      "  * component  (component) object 'Pounds per cow'\n",
      "Dimensions without coordinates: sample\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "series = MonthlyMilkDataset().load()\n",
    "\n",
    "print(series)\n",
    "series.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using a transformer: Rescaling a time series using `Scaler`.\n",
    "\n",
    "Some applications may require your datapoints to be between 0 and 1 (e.g. to feed a time series to a Neural Network based forecasting model). This is easily achieved using the default `Scaler`, which is a wrapper around `sklearn.preprocessing.MinMaxScaler(feature_range=(0, 1))`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<TimeSeries (DataArray) (Month: 168, component: 1, sample: 1)>\n",
      "array([[[0.08653846]],\n",
      "\n",
      "       [[0.01923077]],\n",
      "\n",
      "       [[0.20913462]],\n",
      "\n",
      "       [[0.24759615]],\n",
      "\n",
      "       [[0.41826923]],\n",
      "\n",
      "       [[0.34615385]],\n",
      "\n",
      "       [[0.20913462]],\n",
      "\n",
      "       [[0.11057692]],\n",
      "\n",
      "       [[0.03605769]],\n",
      "\n",
      "       [[0.05769231]],\n",
      "\n",
      "...\n",
      "\n",
      "       [[0.81490385]],\n",
      "\n",
      "       [[0.84134615]],\n",
      "\n",
      "       [[0.99278846]],\n",
      "\n",
      "       [[0.92307692]],\n",
      "\n",
      "       [[0.82451923]],\n",
      "\n",
      "       [[0.73317308]],\n",
      "\n",
      "       [[0.63461538]],\n",
      "\n",
      "       [[0.65865385]],\n",
      "\n",
      "       [[0.58653846]],\n",
      "\n",
      "       [[0.69711538]]])\n",
      "Coordinates:\n",
      "  * Month      (Month) datetime64[ns] 1962-01-01 1962-02-01 ... 1975-12-01\n",
      "  * component  (component) <U1 '0'\n",
      "Dimensions without coordinates: sample\n"
     ]
    }
   ],
   "source": [
    "scaler = Scaler()\n",
    "rescaled = scaler.fit_transform(series)\n",
    "print(rescaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This scaling can easily be inverted too, by calling `inverse_transform()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<TimeSeries (DataArray) (Month: 168, component: 1, sample: 1)>\n",
      "array([[[589.]],\n",
      "\n",
      "       [[561.]],\n",
      "\n",
      "       [[640.]],\n",
      "\n",
      "       [[656.]],\n",
      "\n",
      "       [[727.]],\n",
      "\n",
      "       [[697.]],\n",
      "\n",
      "       [[640.]],\n",
      "\n",
      "       [[599.]],\n",
      "\n",
      "       [[568.]],\n",
      "\n",
      "       [[577.]],\n",
      "\n",
      "...\n",
      "\n",
      "       [[892.]],\n",
      "\n",
      "       [[903.]],\n",
      "\n",
      "       [[966.]],\n",
      "\n",
      "       [[937.]],\n",
      "\n",
      "       [[896.]],\n",
      "\n",
      "       [[858.]],\n",
      "\n",
      "       [[817.]],\n",
      "\n",
      "       [[827.]],\n",
      "\n",
      "       [[797.]],\n",
      "\n",
      "       [[843.]]])\n",
      "Coordinates:\n",
      "  * Month      (Month) datetime64[ns] 1962-01-01 1962-02-01 ... 1975-12-01\n",
      "  * component  (component) <U1 '0'\n",
      "Dimensions without coordinates: sample\n"
     ]
    }
   ],
   "source": [
    "back = scaler.inverse_transform(rescaled)\n",
    "print(back)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the `Scaler` also allows to specify other scalers in its constructor, as long as they implement `fit()`, `transform()` and `inverse_transform()` methods on `TimeSeries` (typically scalers from `scikit-learn`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Another example : `MissingValuesFiller`\n",
    "\n",
    "Let's look at handling missing values in a dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "incomplete_series = MonthlyMilkIncompleteDataset().load()\n",
    "incomplete_series.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filler = MissingValuesFiller()\n",
    "filled = filler.transform(incomplete_series, method=\"quadratic\")\n",
    "\n",
    "filled.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since `MissingValuesFiller` wraps around `pd.interpolate` by default, we can also provide arguments to the `pd.interpolate()` function when calling `transform()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filled = filler.transform(incomplete_series, method=\"quadratic\")\n",
    "filled.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Mapper` and `InvertibleMapper`: A special kind of transformers\n",
    "\n",
    "Sometimes you may want to perform a simple `map()` function on the data. This can also be done using data transformers. `Mapper` takes in a function and applies it to the data elementwise when calling `transform()`.\n",
    "\n",
    "`InvertibleMapper` also allows to specify an inverse function at creation (if there is one) and provides the `inverse_transform()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff5e3826c10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lin_series = linear_timeseries(start_value=0, end_value=2, length=10)\n",
    "\n",
    "squarer = Mapper(lambda x: x**2)\n",
    "squared = squarer.transform(lin_series)\n",
    "\n",
    "lin_series.plot(label=\"original\")\n",
    "squared.plot(label=\"squared\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More complex (and useful) transformations\n",
    "\n",
    "In the Monthly Milk Production dataset used earlier, some of the difference between the months comes from the fact that some months contain more days than others, resulting in a larger production of milk during those months. This makes the time series more complex, and thus harder to predict. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff5e3bcc790>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training, validation = series.split_before(pd.Timestamp(\"1973-01-01\"))\n",
    "\n",
    "model = ExponentialSmoothing()\n",
    "model.fit(training)\n",
    "forecast = model.predict(36)\n",
    "\n",
    "plt.title(\"MAPE = {:.2f}%\".format(mape(forecast, validation)))\n",
    "series.plot(label=\"actual\")\n",
    "forecast.plot(label=\"forecast\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To take this fact into account and achieve better performance, we could instead:\n",
    "\n",
    "1. Transform the time series to represent the average daily production of milk for each month (instead of the total production per month)\n",
    "2. Make a forecast\n",
    "3. Inverse the transformation\n",
    "\n",
    "Let's see how this would be implemented using `InvertibleMapper` and `pd.timestamp.days_in_month`\n",
    "\n",
    "(Idea taken from [\"Forecasting: principles and Practice\"](https://otexts.com/fpp2/transformations.html) by Hyndman and Athanasopoulos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To transform the time series, we have to divide a monthly value (the data point) by the number of days in the month given by the value's corresponding timestamp.\n",
    "\n",
    "`map()` (and thus `Mapper` / `InvertibleMapper`) makes this convenient by allowing to apply a transformation function which uses both the value and its timestamp to compute the new value: `f(timestamp, value) = new_value`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Transform the time series\n",
    "toDailyAverage = InvertibleMapper(\n",
    "    fn=lambda timestamp, x: x / timestamp.days_in_month,\n",
    "    inverse_fn=lambda timestamp, x: x * timestamp.days_in_month,\n",
    ")\n",
    "\n",
    "dailyAverage = toDailyAverage.transform(series)\n",
    "\n",
    "dailyAverage.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff5e3dd1ac0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a forecast\n",
    "dailyavg_train, dailyavg_val = dailyAverage.split_after(pd.Timestamp(\"1973-01-01\"))\n",
    "\n",
    "model = ExponentialSmoothing()\n",
    "model.fit(dailyavg_train)\n",
    "dailyavg_forecast = model.predict(36)\n",
    "\n",
    "plt.title(\"MAPE = {:.2f}%\".format(mape(dailyavg_forecast, dailyavg_val)))\n",
    "dailyAverage.plot()\n",
    "dailyavg_forecast.plot()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inverse the transformation\n",
    "# Here the forecast is stochastic; so we take the median value\n",
    "forecast = toDailyAverage.inverse_transform(dailyavg_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff5e3fbb280>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"MAPE = {:.2f}%\".format(mape(forecast, validation)))\n",
    "series.plot(label=\"actual\")\n",
    "forecast.plot(label=\"forecast\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chaining transformations : introducing `Pipeline`\n",
    "\n",
    "Now suppose that we both want to apply the above transformation (daily averaging), and rescale the dataset between 0 and 1 to use a Neural Network based forecasting model. Instead of applying these two transformations separately, and then inversing them separately, we can use a `Pipeline`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pipeline = Pipeline([toDailyAverage, scaler])\n",
    "transformed = pipeline.fit_transform(training)\n",
    "transformed.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If all transformations in the pipeline are invertible, the `Pipeline` object is too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "back = pipeline.inverse_transform(transformed)\n",
    "back.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall now the incomplete series from `monthly-milk-incomplete.csv`. Suppose that we want to encapsule all our preprocessing steps into a Pipeline, consisting of: a `MissingValuesFiller` for filling the missing values, and a `Scaler` for scaling the dataset between 0 and 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "incomplete_series = MonthlyMilkIncompleteDataset().load()\n",
    "\n",
    "filler = MissingValuesFiller()\n",
    "scaler = Scaler()\n",
    "\n",
    "pipeline = Pipeline([filler, scaler])\n",
    "transformed = pipeline.fit_transform(incomplete_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose we have trained a Neural Network and produced some predictions. Now, we want to scale back out data. Unfortunately, since the `MissingValuesFiller` is not an `InvertibleDataTransformer` (why on Earth would someone want to insert missing values in the results!?), the inverse transformation will raise an exception: _ValueError: Not all transformers in the pipeline can perform inverse_transform_.\n",
    "\n",
    "Frustrating right? Fortunately, you don't have to re-run everything from scratch, excluding the `MissingValuesFiller` from the `Pipeline`. Instead, you can just set the `partial` argument of the `inverse_transform` method to True. In this case, the inverse transformation will be performed skipping the not invertible transformers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "back = pipeline.inverse_transform(transformed, partial=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing multiple TimeSeries\n",
    "\n",
    "Often, we have to deal with multiple TimeSeries. DARTS supports Sequences of TimeSeries as input to transformers and pipelines, so that you don't have to take care of processing each sample separately. Furthermore, it will take care of storing the parameters used by each scaler while transforming different TimeSeries (e.g., with scalers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "series = MonthlyMilkDataset().load()\n",
    "incomplete_series = MonthlyMilkIncompleteDataset().load()\n",
    "\n",
    "multiple_ts = [incomplete_series, series[:10]]\n",
    "\n",
    "filler = MissingValuesFiller()\n",
    "scaler = Scaler()\n",
    "\n",
    "pipeline = Pipeline([filler, scaler])\n",
    "transformed = pipeline.fit_transform(multiple_ts)\n",
    "\n",
    "for ts in transformed:\n",
    "    ts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "back = pipeline.inverse_transform(transformed, partial=True)\n",
    "for ts in back:\n",
    "    ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monitoring & parallelising data processing\n",
    "\n",
    "Sometimes, we could also have to deal with huge datasets. In this cases, processing each sample sequentially can take quite a long time. Darts can help both monitoring the transformations, and processing multiple samples in parallel, when possible. \n",
    "\n",
    "Setting the `verbose` parameter in each transformer or pipeline while create some progress bars:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6c470e3449d4a45b5ea7d73e87deb0d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Fitting (Basic):   0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "414ea3d20db1445d9228a6d540a44dc2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Transform (Basic):   0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "series = MonthlyMilkIncompleteDataset().load()\n",
    "\n",
    "huge_number_of_series = [series] * 10000\n",
    "\n",
    "scaler = Scaler(verbose=True, name=\"Basic\")\n",
    "\n",
    "transformed = scaler.fit_transform(huge_number_of_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now know for how long we will have to wait. But since nobody loves wasting time waiting, we can leverage multiple cores of our machine to process data in parallel. We can do so by setting the `n_jobs` parameter (same usage as in `sklearn`). \n",
    "\n",
    "**Note**: the speed-up closely depends on the number of available cores and the 'CPU-intensiveness' of the transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "051cc5ee572b4c5bab0ae546d617eab1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Fitting (Faster):   0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "577d15b1243047dda08e3d9e97679cfc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Transform (Faster):   0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setting n_jobs to -1 will make the library using all the cores available in the machine\n",
    "scaler = Scaler(verbose=True, n_jobs=-1, name=\"Faster\")\n",
    "scaler.fit(huge_number_of_series)\n",
    "back = scaler.transform(huge_number_of_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
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 },
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